Abstract：
Deep learning techniques offer improvements in computer-aided diagnosis systems. However, acquiring image domain annotations is challenging due to the knowledge and commitment required of expert pathologists. Pathologists often identify regions in whole slide images with diagnostic relevance rather than examining the entire slide, with a positive correlation between the time spent on these critical image regions and diagnostic accuracy. In this paper, a heatmap is generated to represent pathologists\' viewing patterns during diagnosis and used to guide a deep learning architecture during training. The proposed system outperforms traditional approaches based on color and texture image characteristics, integrating pathologists\' domain expertise to enhance region of interest detection without needing individual case annotations. Evaluating our best model, a U-Net model with a pre-trained ResNet-18 encoder, on a skin biopsy whole slide image dataset for melanoma diagnosis, shows its potential in detecting regions of interest, surpassing conventional methods with an increase of 20%, 11%, 22%, and 12% in precision, recall, F1-score, and Intersection over Union, respectively. In a clinical evaluation, three dermatopathologists agreed on the model\'s effectiveness in replicating pathologists\' diagnostic viewing behavior and accurately identifying critical regions. Finally, our study demonstrates that incorporating heatmaps as supplementary signals can enhance the performance of computer-aided diagnosis systems. Without the availability of eye tracking data, identifying precise focus areas is challenging, but our approach shows promise in assisting pathologists in improving diagnostic accuracy and efficiency, streamlining annotation processes, and aiding the training of new pathologists.
摘要：
深度学习技术改进了计算机辅助诊断系统。然而,由于需要专家病理学家的知识和承诺，因此获取图像域注释具有挑战性。病理学家通常在整个幻灯片图像中识别具有诊断相关性的区域，而不是检查整个幻灯片。在这些关键图像区域上花费的时间与诊断准确性之间呈正相关。在本文中,生成热图以表示病理学家在诊断期间的观察模式，并用于在训练期间指导深度学习架构。所提出的系统优于基于颜色和纹理图像特征的传统方法，整合病理学家\'领域的专业知识，以增强感兴趣的区域检测，而不需要个别病例注释。评估我们最好的模型，带有预训练ResNet-18编码器的U-Net模型，在用于黑色素瘤诊断的皮肤活检整个幻灯片图像数据集上，显示了它在检测感兴趣区域方面的潜力，超过常规方法，增加了20%，11%,22%,精度为12%，召回，F1分数，和十字路口，分别。在临床评估中，3名皮肤病理学家同意该模型在复制病理学家的诊断观察行为和准确识别关键区域方面的有效性。最后,我们的研究表明，结合热图作为补充信号可以提高计算机辅助诊断系统的性能。如果没有眼动追踪数据，确定精确的焦点区域是具有挑战性的，但是我们的方法在协助病理学家提高诊断准确性和效率方面显示出希望，简化注释过程，并帮助培训新的病理学家。
